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NSF has awarded three more Information Technology Research (ITR)
grants to researchers in this department.

First, Michael Black was granted $446K over three years for ``The Computer
Science of Biologically Embedded Systems'', work done in conjunction with John
Donoghue (Biomed-Neuroscience) and Lucien Bienenstock (Division of Applied
Mathematics). Michael's abstract states, `` Biologically embedded
systems that
directly couple artificial computational devices with neural
systems are
emerging as a new area of information technology research.
The physical
structure and adaptability of the human brain make these
biologically embedded
systems quite different from computational systems typically studied in
Computer Science.

``Fundamentally, biologically embedded systems must make inferences
about the
behavior of a biological system based on measurements of neural
activity that
are indirect, ambiguous, and uncertain. Moreover. these systems
must adapt to
short- and long-term changes in neural activity of the brain. These
problems
are addressed by a multidisciplinary team in the context of developing a robot
arm that is controlled by simultaneous recordings from neurons in the motor
cortex of a subject. The goal is to model the behavior of these neurons
probabilistically as a function of arm motion and then reconstruct continuous
arm trajectories based on the neural activity. To do so, the project will
exploit mathematical and computational techniques from computer vision, image
processing, and machine learning.

``This work will enhance scientific knowledge about how to design and build new
types of hybrid human/computer systems, will explore new devices to assist the
severely disabled, will address the information technology questions raised by
these biologically embedded systems, and will contribute to the understanding
of neural coding.''

Second, Eli Upfal, working with Michael Mitzenmacher at Harvard, has
been
awarded $524K over five years for research on ``Algorithmic Issues in
Large-Scale Dynamic Networks''. Eli summarizes his work as follows: ``We
propose to develop a theoretically well-founded framework for the design and
analysis of algorithms for large-scale dynamic networks, in particular, for the
Web and related dynamic networks, such as the underlying Internet topology and
Internet-based peer-to-peer ad hoc networks. We plan to develop rigorous
mathematical models that capture key characteristics and can make reliable
predictions about features such as connectivity, information content, and
dynamic of these networks. We plan to apply this framework to test existing
algorithms and construct improved new algorithms.

``The main benefits of developing the mathematical models of the Web structure
and dynamics will be the improved theoretical foundation for the design,
analysis, and testing of algorithms that operate in the Web environment. The
tangible results of this work will therefore be models that can be subjected to
experimental verification, analyses of algorithms based upon these models, new
algorithms that benefit from these analyses, and, finally, proof-of-concept
demonstrations and experimental evaluations of such algorithms.''

Third, Eugene Charniak received $450K over three years for work on ``Learning
Syntactic/Semantic Information for Parsing''. Eugene says, ``The research
envisioned under this grant concerns the unsupervised learning of structural
information about English that is not present in current tree-banks
(specifically the various Penn tree-banks). That is, one wants a machine to
learn this information without having to create a corpus in which the
information is annotated. Generally unsupervised (as opposed to supervised)
learning is of more interest because ultimately such research may shed light on
the larger problems of learning a complete grammar for English, because the
creation of significant corpora is a very labor-intensive task that should be
avoided if at all possible, and because quite often the subdomains in question
are areas of theoretical dispute, so obtaining the agreement necessary prior to
a corpus-creation project might be difficult.

``This proposal is called `Learning Syntactic/Semantic Information for Parsing'
because the structural information to be learned often falls at the boundary
between syntax and semantics. For example, is the fact that `Fred' is
typically a person's first name a syntactic or semantic fact? Does the fact
that `New York Stock Exchange' has as part of its name the location `New York'
fall under syntax or semantics? What about the similarity between the
expressions `[to] market useless items' and `the market for useless items'?
These are some of the topics that come up in this research.

``As for the `for Parsing' portion of the title, the intention is to learn the
above kinds of information in a form that current statistical parsers can use
so that they can output more finely structured parses. However, this is not
meant to suggest that parsing is the sole use for this sort of information --
exactly the opposite is the case. For example, more and more systems for
automatically extracting information from free text use coreference detection
and `named-entity recognition' (e.g., recognizing that `New York' is a location
but `New York Stock Exchange' is an organization). There is evidence to
suggest that both coreference and named-entity recognition can be improved with
the finer level of analysis to be made possible by this research. Or again,
`language models' (programs that assign a probability to strings in a language)
are standard parts of all current speech-recognition systems. There is now
evidence suggesting that finer-grained syntactic analysis can improve current
language models. Thus this research will enable a wide variety of systems to
make better use of language input and thus make these systems more accessible
to a diverse user pool.''